Microsoft leaders on AI's impact on software development

I just realized something about Satya Nadella's bombshell stat from April. 30% of Microsoft's code is now AI-generated. But here's what most people miss: This isn't about replacing engineers. It's about liberating them. Think about it. Microsoft's developers aren't writing boilerplate CRUD operations anymore. They're solving the hard problems. The architecture decisions. The edge cases that break systems. The boring stuff? AI handles it. But here's the catch most teams get wrong: AI won't read your mind. You need to treat it like you're briefing a senior developer. Instead of: "Write an email validator" Try: "Write a Python function that validates email addresses, handles international domains, includes detailed error messages, and follows enterprise patterns." → Be brutally specific → Define the requirements → Clarify the context The engineers who master this human-AI collaboration will build the next generation of software. The ones who don't? They'll spend the next decade playing catch-up. How are you using AI in your development workflow? What's worked (or failed) for you?

🚀 Microsoft Just Changed the Game for AI Development Microsoft has unveiled its Agent Framework—a powerful AI toolkit designed to help developers build intelligent agents that can automate decision-making and streamline workflows in software engineering. 🤖 This is huge for the developer community. Here's why: 👇 What It Enables: ✨ The Agent Framework empowers developers to create AI agents that don't just respond to commands—they think, decide, and act autonomously within complex workflows. Imagine agents that can analyze code repositories, identify bottlenecks, suggest optimizations, and even implement fixes with minimal human intervention. 💡 Why This Matters: 🎯 We're moving beyond simple automation into true intelligent assistance. Software teams can now build agents that understand context, make informed decisions, and handle multi-step processes that previously required constant human oversight. This means faster development cycles, fewer repetitive tasks, and more time for creative problem-solving. ⚡ The Bigger Picture: 🌐 This framework is part of the broader shift toward agentic AI—systems that can operate with a degree of autonomy while remaining aligned with human goals. For software engineering, this could mean agents that manage deployments, conduct code reviews, monitor system health, or coordinate across development tools seamlessly. 🔧 The question isn't whether AI agents will transform how we build software—it's how quickly we'll adapt to working alongside them. 🚀 What are your thoughts on AI agents in software development? Excited about the possibilities or cautious about the implications? 💭 #AI #Microsoft #SoftwareEngineering #ArtificialIntelligence #DevTools #Innovation #Technology #AgenticAI

Another big player just entered the agent space! Seems like it’s the week for big announcements (Isn’t every week like that!!) Amazon has launched Quick Suite and AgentCore, which seem to be their answer to Microsoft’s Copilot and Google’s Gemini. It’s designed for enterprise teams who want AI that can act, not just chat. Agents can analyse data, summarise insights, and take action across tools like Slack, Salesforce, and AWS, all within secure, private environments. The interesting part is how open it seems. You can build custom, domain-specific agents that understand your business data and workflows, not just generic prompts. If it delivers what it promises, private, contextual AI that connects systems and gets things done, this could be a quiet but important shift in how enterprise AI actually works day-to-day. Has anyone had a chance to try it yet? I’m curious how it compares to Copilot or Gemini in real-world use. -- 👋 I’m Adam, a consultant and advisor helping leaders fix fragile systems and solve organisational problems with the right mix of process, platforms and AI. Founder of Cogworks, and currently exploring AI-assisted development in my Vibe Coding experiments. If your digital stack is holding you back, let’s talk.

The agentic AI space is moving fast OR maybe too fast. I've watched teams chase every new framework drop, only to rewrite their stack 3 months later. Exhausting for devs, expensive for businesses. Microsoft's trajectory is a useful case study: → Semantic Kernel (enterprise SDK) - 2023 → AutoGen (multi-agent orchestration)   → Microsoft Agent Framework (unified runtime) - 2025 Three releases, one clear direction. But only now is the story stable enough to bet on. My take for developers: Don't try to learn everything end-to-end. You'll burn out. Instead, observe. Watch what breaks, what gets fixed, what patterns emerge across releases. When you observe, you learn the limitations, and that tells you when a better version is coming. Go deep only when a framework shows real stability: documented migrations, production stories, and "momentum that lasts more than a quarter." For engineering leaders: Let the dust settle. Pilot narrowly, measure obsessively (reliability + observability), scale only when proven. No one gets promoted for adopting bleeding-edge tech that breaks in production. If you're starting fresh today, the Microsoft Agent Framework is worth exploring, after you validate it against your actual use case. Patience isn't slow. It's strategic. What's your approach? would love to hear your thoughts! Observing or diving in immediately? #AgenticAI #AI #EngineeringLeadership #Microsoft #AgentFramework #autogen #SemanticKernel

For over 10 years, my world has been about building applications. Now, I believe the future isn't just about apps, it's about intelligence. My journey from full-stack development to DevOps has shown me one constant: a relentless drive for efficiency. But today, the game has changed. We've moved beyond simple CI/CD pipelines and basic automation. We're in the era of Intelligent Automation. The tools are no longer just for hyperspecialized data science teams. The new "Intelligent Automation Stack" is here, and it's incredibly accessible. For me, that stack is: 🔹 Google Cloud Platform: For scalable, enterprise-grade power. 🔹 n8n: As the powerful orchestration layer to connect any service with ease. 🔹 State-of-the-Art LLMs: As the "brain" to handle tasks that once required human cognition—like understanding text, making decisions, and generating content. By combining these, we can now build sophisticated, AI-powered workflows in days, not months. We can automate the non-linear, complex processes that were previously out of reach. This is the shift I'm dedicating my focus to. What's the most impactful automation you've seen that uses AI? I'm curious to hear what problems others are solving! #AI #IntelligentAutomation #GoogleCloud #n8n #LLM #GenerativeAI #DigitalTransformation #FutureOfWork

🚀 Exciting News in AI Development! Microsoft has just released their Agent Framework in public preview - and this is a game-changer for enterprise AI! What makes this special? ✅ Unified Platform: Combines the best of Semantic Kernel and AutoGen into one enterprise-ready SDK ✅ Multi-Language Support: Full support for both Python and .NET implementations ✅ Graph-Based Workflows: Sophisticated orchestration with streaming, checkpointing, and human-in-the-loop capabilities ✅ Enterprise-Ready: Built-in observability, compliance, and governance features from day one ✅ Production-Ready: Already being tested by industry leaders like KPMG, TCS, Commerzbank, and Citrix Why this matters now: While frameworks like LangChain, CrewAI, and LangGraph excel in their niches, Microsoft Agent Framework bridges the gap between research innovation and enterprise production. It's not just about building agents - it's about running them responsibly at scale with the trust, governance, and interoperability that enterprises demand. Key differentiators: 🔹 Open standards support (MCP, Agent-to-Agent protocols) 🔹 Cloud-agnostic runtime (containers, on-premises, multi-cloud) 🔹 Advanced multi-agent orchestration patterns 🔹 Extensible architecture with enterprise system connectors 🔹 Built-in responsible AI guardrails and PII detection The Agentic Economy is shifting from experimentation to enterprise reality. This framework positions Microsoft at the forefront of making multi-agent AI systems truly enterprise-grade. Github link in the comments. #AI #AgenticAI #Microsoft #MultiAgent #MachineLearning #Enterprise #Innovation #TechLeadership

90% adoption doesn't mean no challenges. Google Cloud’s latest report puts AI use among software professionals at 90%, up 14% from last year. Developers now spend a median two hours a day with AI in core workflows. There is no hiding from AI if you write code. The friction is not adoption. It is operations. Here is where teams are getting stuck, and what actually helps: - Tool sprawl: jumping between multiple assistants kills flow. Pick a default assistant in the IDE, standardise when to use others. - No codebase context: chat without your repo leads to wrong suggestions. Index the code, enable repo-aware help, and require inline citations to source. - Code review trust: reviewers do not trust AI diffs. Label AI-generated changes, require explanations and tests alongside suggestions. - Privacy gaps: pasting sensitive code into public models is still happening. Use private endpoints, redaction, and clear data-use policies in the editor. - Prompt drift and rework: prompts live in Notion and Slack. Treat prompts as code. Version them, test them, and ship them with the repo. At Codedevza AI we maintain a standard prompt pack per codebase for repeatable results. - No measurement: two hours a day feels productive, but is it? Track PR cycle time, defect rates, and time to first review. Reward outcomes, not usage hours. AI is already in the room. The teams that win are boring about it: fewer tools, clearer defaults, measurable outcomes. #SoftwareEngineering #AIEngineering #DeveloperExperience #DevTools #EngineeringLeadership

Fact : AI is responsible for generating more than 30% of the code at Google & Microsoft. And... I am on a journey to find out how they manage to do it "confidently", "consistently", and optionally "in the most cost efficient way". Doesn't matter whether it is a Greenfield or a Brownfield project, there is sure to have certain advantages moving into an AI development workflow. This is one of the many research, reading and testing that I do when I am "not teaching". Otherwise how I would I still stay relevant in the software development space in a very fast moving AI development scene? Lucky for me, it is very fortunate that I can tap into experience (and senses) developed in a similar capacity at different stages of a Software Development Life Cycle (maybe 100 moons ago). So with a little rework for the new terminologies, I should be able to catch on... Do I get tired trying to map stuff with my previous experience? No... In fact it makes me more excited on what I would discover, and I can't wait to share this in my upcoming workshop for "AI For Software Developers". My peers would agree that this is indeed a strange time to "stay in Software Engineering" or "move into Software Engineering" space. Some are even thinking of "moving on from Software Engineering" into a more Product Management Role. Is there any room for humans anymore? Will we end up building systems that will make us obsolete? If anyone in my network has any unanswered / curious questions about this subject matter, would really love it if you can drop me a comment below. 🙏

Elon's new company Macrohard will use AI to compete with Microsoft and other software developers... "In principle, given that software companies like Microsoft do not themselves manufacture any physical hardware, it should be possible to simulate them entirely with AI." https://lnkd.in/gJzX_g4n